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- PaperPrivate Retrieval Augmented Generation via Random Projection
Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it can also introduce p
- PaperBackward Compatibility in Zero-Bit Neural Audio Watermarking
Neural watermarking systems are typically trained as tightly coupled embedder-detector pairs and implicitly assume static deployment. In real-world deployments, embedders
- PaperGrounded Human-Attributed Description and Activity Recognition in Videos
Understanding characteristics and activities of individuals in complex multi-person environments is crucial for real-world applications. However, existing datasets often
- PaperMulti-Target Few-Shot Prompting with Diffusion Language Models
Few-shot prompting is useful when instructions and rewards are hard to specify. One such application is editing by example, where a demonstrated formatting change or API
- PaperTowards Continual Learning: Mitigating Forgetting With Subspace Tuning
Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle
- PaperCAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty
Large language models (LLMs) are becoming prevalent in scenarios where honesty is critical. However, despite alignment training, these models still exhibit dishonest beha
- PaperEliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search
Reinforcement learning has become a common post-training technique to improve the accuracy of LLMs on verifiable tasks. However, such training can narrow the set of solut
- PaperImproving Environment-Agnostic Coding Agents: The Role of Scaffolding
Stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding. We investigate why this ad
- PaperLearning Rational Strategies for Temporal Objectives in Multi-Agent Systems
We study rational synthesis for stochastic multi-agent systems with unknown dynamics, where each agent is equipped with its own probabilistic temporal objective specified
- PaperAsking Smarter Questions: Targeted Active Preference Learning for Underspecified Rewards
Reward learning typically assumes the human's input covers everything that matters for the task. In practice, humans can only attend to so much at once, so their teaching
- PaperRouting Policy Optimization for Online Diffusion Reinforcement Learning
Reinforcement learning can align visual generators with reward signals beyond their pretraining objectives, but existing score-function methods typically introduce stocha
- PaperLEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents
Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alone often fails. Wea
- PaperMJev-Spatial: System One Spatial Intelligence via Native Attention Matching
Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial relation holds, how
- PaperLibrarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries
Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottlenecking agents with
- PaperUnsupervised Clustering of Public Transport Vehicles Using Deep Morphological Features and Color Histograms
Surveillance cameras along urban roads produce large volumes of unlabeled video that could support the monitoring of public transport, but grouping the observed vehicles
- PaperX-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning
The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control. While the RAN Intelligent Controller (RIC) enables centralized optim
- PaperBeyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling
Strong guidance in text-to-image (T2I) diffusion models concentrates sampling toward high-density modes of the conditional distribution, collapsing independently sampled
- PaperSCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image Generation
Reinforcement learning (RL) enables language models to improve text-to-image (T2I) generation by rewriting prompts for a frozen image generator. Existing methods typicall
- PaperBaselines and Score Centering in Policy Gradient Estimation
Reinforcement learning has become a widely used approach to post-training large language models. In a typical implementation, learning is off-policy: The policy generatin
- PaperTopology-Inspired Fine-Tuning for Wind-Field Super-Resolution
Scientific super-resolution (SR) models are typically optimized with pointwise reconstruction losses, yet scientific interpretation often hinges on extrema, connected reg
- PaperFPG-Seg: Annotation-free Retinal Vessel Topology Segmentation
State-of-the art approaches to retinal vessel segmentation typically require extensive annotations, a highly time-consuming process, reliant on clinical expertise. We pre
- PaperDenoising Textures is a Different Ballgame Altogether!
The human brain effortlessly deciphers colors and textures from visual stimuli and reconstructs the world around us, yet preserving such details remains challenging in im
- PaperBitSparse: Compact Activation Storage for ReLU Feed-Forward Networks
Reducing memory use in large language model (LLM) training is an ongoing challenge. Activation checkpointing saves memory by recomputation, while lossy compression change
- PaperExplore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits
We study how to evaluate $K$ target policies simultaneously in stochastic contextual bandits with linear reward structure. Given a known $d$-dimensional feature map and r
- PaperNot X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness
Recurring expressions in model-written text are often treated as signatures of machine authorship, yet most of them serve legitimate functions. Whether such an expression
- PaperDisagreement-Guided Weak-to-Strong Routing
Small language models are increasingly deployed on edge devices and robotic systems to meet cost and latency constraints, yet their limited capabilities can make reliable
- PaperLanguage Is a Security Surface for Agent Guards
Step-level guards decide, before each tool call, whether an agent's next action is safe given the user's request and the trajectory so far. Users write those requests in
- PaperShortcut Sensitivity in Agent-Safety Evaluations
AI agents take actions through tools. Some actions are sensitive, so guards that supervise them must themselves be evaluated reliably. Yet the data used to evaluate a gua
- PaperSurvICL: a Foundation Model for in-context Survival Analysis
Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression. Yet in survival analysis, which predicts when an event occurs fro
- PaperMMSearch-OPD: Easy Made Hard, Hard Made Learnable for Long-Horizon Multimodal Search Agents
Multimodal deep search is moving from short queries to long-horizon investigations that continually acquire evidence. However, training such agents remains constrained by
- PaperQuery-Efficient Rejection Sampling for Parallel Masked Diffusion
Masked diffusion models (MDMs) accelerate generation by unmasking multiple tokens in parallel, but sampling these tokens independently ignores their correlations and bias
- PaperTraining-Free On-Policy Distillation
On-policy distillation consolidates specialist models into one student at the price of rollouts, teacher passes and gradient steps for every teacher. We show that the con
- PaperOS-Helix: Unifying GUI Interaction and Code Execution in Native Computer-Using Agents
Real desktop automation is neither purely visual nor purely programmatic. Agents must understand user-facing interfaces while efficiently manipulating files, scripts, and
- PaperWaveLLM: Wavelet-Domain LLM Entropy Modeling for Lossless Image Compression
Lossless image compression fundamentally relies on accurate entropy modeling, driving a recent shift toward large language model (LLM)-based methods. However, existing ap
- PaperArchitecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs
Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in. However, the extensive
- PaperHow Much Physics Can Vision Learn from Audio? Probing and Distilling Damping Structure
Physical understanding is crucial for decision making, yet vision models struggle to infer properties such as damping that govern how materials behave. Audio encodes such
- PaperSkill-Decoupled On-Policy Expert Distillation for Unified Vision-Language Models
Reinforcement learning (RL) can elicit specialized visual capabilities in vision-language models (VLMs), yet jointly optimizing heterogeneous objectives often introduces
- PaperFutureLift: Lifting Trajectory Proposals into Bidirectional Future Graphs for Motion Forecasting
Accurate motion prediction is critical for safe autonomous driving, yet even strong forecasters leave residual errors in their multimodal proposals. Existing refiners com
- PaperGNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP
Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature sizes shrink. Directed
- PaperGeneralization Guarantees of Randomized Block Coordinate Descent for Nonconvex Learning
Randomized block coordinate descent (RBCD) is a popular optimization algorithm for many large-scale machine learning problems. The existing theoretical analysis of RBCD m
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